{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "0b9ffbb2-8d33-eb0c-5a42-0733bdc3b74b",
    "_uuid": "e14f65723f5ac826104e861c45e58525740d8b2a"
   },
   "source": [
    "# Bikeshare数据集上的数据探索\n",
    "\n",
    "1、\t任务描述\n",
    "请在Capital Bikeshare （美国Washington, D.C.的一个共享单车公司）提供的自行车数据上进行回归分析。根据每天的天气信息，预测该天的单车共享骑行量。\n",
    "\n",
    "原始数据集地址：http://archive.ics.uci.edu/ml/datasets/Bike+Sharing+Dataset\n",
    "1)\t文件说明\n",
    "day.csv: 按天计的单车共享次数（作业只需使用该文件）\n",
    "hour.csv: 按小时计的单车共享次数（无需理会）\n",
    "readme：数据说明文件\n",
    "\n",
    "2)\t字段说明\n",
    "Instant记录号\n",
    "Dteday：日期\n",
    "Season：季节（1=春天、2=夏天、3=秋天、4=冬天）\n",
    "yr：年份，(0: 2011, 1:2012)\n",
    "mnth：月份( 1 to 12)\n",
    "hr：小时 (0 to 23)  （只在hour.csv有，作业忽略此字段）\n",
    "holiday：是否是节假日\n",
    "weekday：星期中的哪天，取值为0～6\n",
    "workingday：是否工作日\n",
    "1=工作日 （是否为工作日，1为工作日，0为非周末或节假日\n",
    "weathersit：天气（1：晴天，多云 ",
    "2：雾天，阴天 ",
    "3：小雪，小雨 ",
    "4：大雨，大雪，大雾）\n",
    "temp：气温摄氏度\n",
    "atemp：体感温度\n",
    "hum：湿度\n",
    "windspeed：风速\n",
    "casual：非注册用户个数\n",
    "registered：注册用户个数\n",
    "cnt：给定日期（天）时间（每小时）总租车人数，响应变量y （cnt = casual + registered）\n",
    "\n",
    "casual、registered和cnt三个特征均为要预测的y，作业里只需对cnt进行预测"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 导入必要的工具包"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "_cell_guid": "2ba3154a-c2aa-3158-1984-63ad2c0c786a",
    "_uuid": "5eb696b95780825e94ddb49787f9fa339fc3833b"
   },
   "outputs": [],
   "source": [
    "# 数据读取及基本处理\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "# plotting\n",
    "import seaborn as sn\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "# setting params\n",
    "params = {'legend.fontsize': 'x-large',\n",
    "          'figure.figsize': (30, 10),\n",
    "          'axes.labelsize': 'x-large',\n",
    "          'axes.titlesize':'x-large',\n",
    "          'xtick.labelsize':'x-large',\n",
    "          'ytick.labelsize':'x-large'}\n",
    "\n",
    "#设定背景为白\n",
    "sn.set_style('whitegrid')\n",
    "sn.set_context('talk')\n",
    "\n",
    "plt.rcParams.update(params)\n",
    "pd.options.display.max_colwidth = 600\n",
    "\n",
    "# pandas display data frames as tables\n",
    "from IPython.display import display, HTML"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 读入数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "_cell_guid": "21fa35be-878b-b4f2-ef6e-68dc070b8bfa",
    "_uuid": "73aee228226be55c0c8a6e4fcbf818c56cd94926"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>instant</th>\n",
       "      <th>dteday</th>\n",
       "      <th>season</th>\n",
       "      <th>yr</th>\n",
       "      <th>mnth</th>\n",
       "      <th>holiday</th>\n",
       "      <th>weekday</th>\n",
       "      <th>workingday</th>\n",
       "      <th>weathersit</th>\n",
       "      <th>temp</th>\n",
       "      <th>atemp</th>\n",
       "      <th>hum</th>\n",
       "      <th>windspeed</th>\n",
       "      <th>casual</th>\n",
       "      <th>registered</th>\n",
       "      <th>cnt</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>2011-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.344167</td>\n",
       "      <td>0.363625</td>\n",
       "      <td>0.805833</td>\n",
       "      <td>0.160446</td>\n",
       "      <td>331</td>\n",
       "      <td>654</td>\n",
       "      <td>985</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>2011-01-02</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.363478</td>\n",
       "      <td>0.353739</td>\n",
       "      <td>0.696087</td>\n",
       "      <td>0.248539</td>\n",
       "      <td>131</td>\n",
       "      <td>670</td>\n",
       "      <td>801</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>2011-01-03</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.196364</td>\n",
       "      <td>0.189405</td>\n",
       "      <td>0.437273</td>\n",
       "      <td>0.248309</td>\n",
       "      <td>120</td>\n",
       "      <td>1229</td>\n",
       "      <td>1349</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>2011-01-04</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>0.212122</td>\n",
       "      <td>0.590435</td>\n",
       "      <td>0.160296</td>\n",
       "      <td>108</td>\n",
       "      <td>1454</td>\n",
       "      <td>1562</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>2011-01-05</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.226957</td>\n",
       "      <td>0.229270</td>\n",
       "      <td>0.436957</td>\n",
       "      <td>0.186900</td>\n",
       "      <td>82</td>\n",
       "      <td>1518</td>\n",
       "      <td>1600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>2011-01-06</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.204348</td>\n",
       "      <td>0.233209</td>\n",
       "      <td>0.518261</td>\n",
       "      <td>0.089565</td>\n",
       "      <td>88</td>\n",
       "      <td>1518</td>\n",
       "      <td>1606</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>2011-01-07</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0.196522</td>\n",
       "      <td>0.208839</td>\n",
       "      <td>0.498696</td>\n",
       "      <td>0.168726</td>\n",
       "      <td>148</td>\n",
       "      <td>1362</td>\n",
       "      <td>1510</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>2011-01-08</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.165000</td>\n",
       "      <td>0.162254</td>\n",
       "      <td>0.535833</td>\n",
       "      <td>0.266804</td>\n",
       "      <td>68</td>\n",
       "      <td>891</td>\n",
       "      <td>959</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9</td>\n",
       "      <td>2011-01-09</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.138333</td>\n",
       "      <td>0.116175</td>\n",
       "      <td>0.434167</td>\n",
       "      <td>0.361950</td>\n",
       "      <td>54</td>\n",
       "      <td>768</td>\n",
       "      <td>822</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10</td>\n",
       "      <td>2011-01-10</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.150833</td>\n",
       "      <td>0.150888</td>\n",
       "      <td>0.482917</td>\n",
       "      <td>0.223267</td>\n",
       "      <td>41</td>\n",
       "      <td>1280</td>\n",
       "      <td>1321</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   instant      dteday  season  yr  mnth  holiday  weekday  workingday  \\\n",
       "0        1  2011-01-01       1   0     1        0        6           0   \n",
       "1        2  2011-01-02       1   0     1        0        0           0   \n",
       "2        3  2011-01-03       1   0     1        0        1           1   \n",
       "3        4  2011-01-04       1   0     1        0        2           1   \n",
       "4        5  2011-01-05       1   0     1        0        3           1   \n",
       "5        6  2011-01-06       1   0     1        0        4           1   \n",
       "6        7  2011-01-07       1   0     1        0        5           1   \n",
       "7        8  2011-01-08       1   0     1        0        6           0   \n",
       "8        9  2011-01-09       1   0     1        0        0           0   \n",
       "9       10  2011-01-10       1   0     1        0        1           1   \n",
       "\n",
       "   weathersit      temp     atemp       hum  windspeed  casual  registered  \\\n",
       "0           2  0.344167  0.363625  0.805833   0.160446     331         654   \n",
       "1           2  0.363478  0.353739  0.696087   0.248539     131         670   \n",
       "2           1  0.196364  0.189405  0.437273   0.248309     120        1229   \n",
       "3           1  0.200000  0.212122  0.590435   0.160296     108        1454   \n",
       "4           1  0.226957  0.229270  0.436957   0.186900      82        1518   \n",
       "5           1  0.204348  0.233209  0.518261   0.089565      88        1518   \n",
       "6           2  0.196522  0.208839  0.498696   0.168726     148        1362   \n",
       "7           2  0.165000  0.162254  0.535833   0.266804      68         891   \n",
       "8           1  0.138333  0.116175  0.434167   0.361950      54         768   \n",
       "9           1  0.150833  0.150888  0.482917   0.223267      41        1280   \n",
       "\n",
       "    cnt  \n",
       "0   985  \n",
       "1   801  \n",
       "2  1349  \n",
       "3  1562  \n",
       "4  1600  \n",
       "5  1606  \n",
       "6  1510  \n",
       "7   959  \n",
       "8   822  \n",
       "9  1321  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 读入数据\n",
    "train = pd.read_csv(\"day.csv\")\n",
    "train.head(10)\n",
    "#print(\"train : \" + str(train.shape))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 731 entries, 0 to 730\n",
      "Data columns (total 16 columns):\n",
      "instant       731 non-null int64\n",
      "dteday        731 non-null object\n",
      "season        731 non-null int64\n",
      "yr            731 non-null int64\n",
      "mnth          731 non-null int64\n",
      "holiday       731 non-null int64\n",
      "weekday       731 non-null int64\n",
      "workingday    731 non-null int64\n",
      "weathersit    731 non-null int64\n",
      "temp          731 non-null float64\n",
      "atemp         731 non-null float64\n",
      "hum           731 non-null float64\n",
      "windspeed     731 non-null float64\n",
      "casual        731 non-null int64\n",
      "registered    731 non-null int64\n",
      "cnt           731 non-null int64\n",
      "dtypes: float64(4), int64(11), object(1)\n",
      "memory usage: 91.5+ KB\n"
     ]
    }
   ],
   "source": [
    "train.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "没有缺失数据"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "be11c1c7-acfe-cb9a-bc81-95d461b884c5",
    "_uuid": "b53750ea6a3a2a02aa4297f5401e29c2f4d92e31"
   },
   "source": [
    "## 数据探索"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>instant</th>\n",
       "      <th>season</th>\n",
       "      <th>yr</th>\n",
       "      <th>mnth</th>\n",
       "      <th>holiday</th>\n",
       "      <th>weekday</th>\n",
       "      <th>workingday</th>\n",
       "      <th>weathersit</th>\n",
       "      <th>temp</th>\n",
       "      <th>atemp</th>\n",
       "      <th>hum</th>\n",
       "      <th>windspeed</th>\n",
       "      <th>casual</th>\n",
       "      <th>registered</th>\n",
       "      <th>cnt</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>366.000000</td>\n",
       "      <td>2.496580</td>\n",
       "      <td>0.500684</td>\n",
       "      <td>6.519836</td>\n",
       "      <td>0.028728</td>\n",
       "      <td>2.997264</td>\n",
       "      <td>0.683995</td>\n",
       "      <td>1.395349</td>\n",
       "      <td>0.495385</td>\n",
       "      <td>0.474354</td>\n",
       "      <td>0.627894</td>\n",
       "      <td>0.190486</td>\n",
       "      <td>848.176471</td>\n",
       "      <td>3656.172367</td>\n",
       "      <td>4504.348837</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>211.165812</td>\n",
       "      <td>1.110807</td>\n",
       "      <td>0.500342</td>\n",
       "      <td>3.451913</td>\n",
       "      <td>0.167155</td>\n",
       "      <td>2.004787</td>\n",
       "      <td>0.465233</td>\n",
       "      <td>0.544894</td>\n",
       "      <td>0.183051</td>\n",
       "      <td>0.162961</td>\n",
       "      <td>0.142429</td>\n",
       "      <td>0.077498</td>\n",
       "      <td>686.622488</td>\n",
       "      <td>1560.256377</td>\n",
       "      <td>1937.211452</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.059130</td>\n",
       "      <td>0.079070</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.022392</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>22.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>183.500000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.337083</td>\n",
       "      <td>0.337842</td>\n",
       "      <td>0.520000</td>\n",
       "      <td>0.134950</td>\n",
       "      <td>315.500000</td>\n",
       "      <td>2497.000000</td>\n",
       "      <td>3152.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>366.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.498333</td>\n",
       "      <td>0.486733</td>\n",
       "      <td>0.626667</td>\n",
       "      <td>0.180975</td>\n",
       "      <td>713.000000</td>\n",
       "      <td>3662.000000</td>\n",
       "      <td>4548.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>548.500000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.655417</td>\n",
       "      <td>0.608602</td>\n",
       "      <td>0.730209</td>\n",
       "      <td>0.233214</td>\n",
       "      <td>1096.000000</td>\n",
       "      <td>4776.500000</td>\n",
       "      <td>5956.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>731.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.861667</td>\n",
       "      <td>0.840896</td>\n",
       "      <td>0.972500</td>\n",
       "      <td>0.507463</td>\n",
       "      <td>3410.000000</td>\n",
       "      <td>6946.000000</td>\n",
       "      <td>8714.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          instant      season          yr        mnth     holiday     weekday  \\\n",
       "count  731.000000  731.000000  731.000000  731.000000  731.000000  731.000000   \n",
       "mean   366.000000    2.496580    0.500684    6.519836    0.028728    2.997264   \n",
       "std    211.165812    1.110807    0.500342    3.451913    0.167155    2.004787   \n",
       "min      1.000000    1.000000    0.000000    1.000000    0.000000    0.000000   \n",
       "25%    183.500000    2.000000    0.000000    4.000000    0.000000    1.000000   \n",
       "50%    366.000000    3.000000    1.000000    7.000000    0.000000    3.000000   \n",
       "75%    548.500000    3.000000    1.000000   10.000000    0.000000    5.000000   \n",
       "max    731.000000    4.000000    1.000000   12.000000    1.000000    6.000000   \n",
       "\n",
       "       workingday  weathersit        temp       atemp         hum   windspeed  \\\n",
       "count  731.000000  731.000000  731.000000  731.000000  731.000000  731.000000   \n",
       "mean     0.683995    1.395349    0.495385    0.474354    0.627894    0.190486   \n",
       "std      0.465233    0.544894    0.183051    0.162961    0.142429    0.077498   \n",
       "min      0.000000    1.000000    0.059130    0.079070    0.000000    0.022392   \n",
       "25%      0.000000    1.000000    0.337083    0.337842    0.520000    0.134950   \n",
       "50%      1.000000    1.000000    0.498333    0.486733    0.626667    0.180975   \n",
       "75%      1.000000    2.000000    0.655417    0.608602    0.730209    0.233214   \n",
       "max      1.000000    3.000000    0.861667    0.840896    0.972500    0.507463   \n",
       "\n",
       "            casual   registered          cnt  \n",
       "count   731.000000   731.000000   731.000000  \n",
       "mean    848.176471  3656.172367  4504.348837  \n",
       "std     686.622488  1560.256377  1937.211452  \n",
       "min       2.000000    20.000000    22.000000  \n",
       "25%     315.500000  2497.000000  3152.000000  \n",
       "50%     713.000000  3662.000000  4548.000000  \n",
       "75%    1096.000000  4776.500000  5956.000000  \n",
       "max    3410.000000  6946.000000  8714.000000  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#对数据值型特征，用常用统计量观察其分布\n",
    "train.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1. 离散特征的分布"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "season属性的不同取值和出现的次数\n",
      "3    188\n",
      "2    184\n",
      "1    181\n",
      "4    178\n",
      "Name: season, dtype: int64\n",
      "\n",
      "mnth属性的不同取值和出现的次数\n",
      "12    62\n",
      "10    62\n",
      "8     62\n",
      "7     62\n",
      "5     62\n",
      "3     62\n",
      "1     62\n",
      "11    60\n",
      "9     60\n",
      "6     60\n",
      "4     60\n",
      "2     57\n",
      "Name: mnth, dtype: int64\n",
      "\n",
      "weathersit属性的不同取值和出现的次数\n",
      "1    463\n",
      "2    247\n",
      "3     21\n",
      "Name: weathersit, dtype: int64\n",
      "\n",
      "weekday属性的不同取值和出现的次数\n",
      "6    105\n",
      "1    105\n",
      "0    105\n",
      "5    104\n",
      "4    104\n",
      "3    104\n",
      "2    104\n",
      "Name: weekday, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "#对类别型特征，观察其取值范围及直方图\n",
    "categorical_features = ['season','mnth','weathersit','weekday']\n",
    "for col in categorical_features:\n",
    "    print('\\n%s属性的不同取值和出现的次数'%col)\n",
    "    print(train[col].value_counts())\n",
    "    train[col] = train[col].astype('object')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "类别型特征的取值不多\n",
    "类别型特征可以采用独热编码（One hot encoding）/哑编码"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2. 数值特征的分布"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x7fbc918b8240>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7fbc9187a9e8>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x7fbc918a9080>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7fbc918506d8>]],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#对数值型特征，直方图\n",
    "numerical_features = ['temp','atemp','hum','windspeed']\n",
    "train[numerical_features].hist()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3. 特征与目标之间的关系"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 3.1 每年骑行量的分布\n",
    "violinplot中用x表示类别（年）信息"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fbc917a4748>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sn.violinplot(data=train[['yr',\n",
    "                        'cnt']],\n",
    "              x=\"yr\",y=\"cnt\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "2011年和2012年的分布差异很大"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 3.2 一年中每天的骑车量\n",
    "用颜色参数hue表示类别（年）信息"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0.5, 1.0, 'dayly distribution of counts')]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import datetime\n",
    "\n",
    "train['date'] = pd.to_datetime(train['dteday'])\n",
    "train['dayofyear'] = train[\"date\"].dt.dayofyear  #减今年的第几天\n",
    "\n",
    "fig,ax = plt.subplots()\n",
    "sn.pointplot(data=train[['dayofyear',\n",
    "                           'cnt',\n",
    "                           'yr']],\n",
    "             x='dayofyear',y='cnt',\n",
    "             hue='yr',ax=ax)\n",
    "ax.set(title=\"dayly distribution of counts\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "每年开始和结束的数量少，中间多，骑行量和季节/月份有关"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 3.3 季节与骑车数量的关系\n",
    "violinplot得到详细分布"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fbc8ed7e940>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sn.violinplot(data=train[['season',\n",
    "                        'cnt']],\n",
    "              x=\"season\",y=\"cnt\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "能看出来每个季节骑行量的分布不同\n",
    "barplot利用矩阵条的高度反映数值变量的集中趋势，以及使用errorbar功能（差棒图）来估计变量之间的差值统计。\n",
    "谨记barplot展示的是某种变量分布的平均值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0.5, 1.0, 'Seasonly distribution of counts')]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,ax = plt.subplots()\n",
    "sn.barplot(data=train[['season',\n",
    "                       'cnt']],\n",
    "           x=\"season\",y=\"cnt\")\n",
    "ax.set(title=\"Seasonly distribution of counts\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "骑行量和季节的关系就很明显了：第2、3、4季度的骑行量明显高于第1季度"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 3.4 月份与骑车数量的关系"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0.5, 1.0, 'Monthly distribution of counts')]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,ax = plt.subplots()\n",
    "sn.barplot(data=train[['mnth',\n",
    "                       'cnt']],\n",
    "           x=\"mnth\",y=\"cnt\")\n",
    "ax.set(title=\"Monthly distribution of counts\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 3.5 天气和骑车数目的关系"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0.5, 1.0, 'weathersit distribution of counts')]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,ax = plt.subplots()\n",
    "sn.barplot(data=train[['weathersit',\n",
    "                       'cnt']],\n",
    "           x=\"weathersit\",y=\"cnt\")\n",
    "ax.set(title=\"weathersit distribution of counts\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 3.6 工作日和节假日的分布"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fbc8ea48630>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,(ax1,ax2) = plt.subplots(ncols=2)\n",
    "sn.barplot(data=train,x='holiday',y='cnt',ax=ax1)\n",
    "sn.barplot(data=train,x='workingday',y='cnt',ax=ax2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 3.7 数值型特征和y之间的相关性"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fbc8eaa23c8>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "corrMatt = train[[\"temp\",\"atemp\",\n",
    "                  \"hum\",\"windspeed\",\n",
    "                  \"casual\",\"registered\",\n",
    "                  \"cnt\"]].corr()\n",
    "mask = np.array(corrMatt)\n",
    "mask[np.tril_indices_from(mask)] = False\n",
    "sn.heatmap(corrMatt, mask=mask,\n",
    "           vmax=.8, square=True,annot=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "体感温度和温度高度相关\n",
    "目标cnt与温度正相关、湿度和风速负相关"
   ]
  }
 ],
 "metadata": {
  "_change_revision": 0,
  "_is_fork": false,
  "celltoolbar": "附件",
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
